Fixed resolution display devices require the display data to have a certain vertical and horizontal resolution, in terms of the number of rows and columns of pixels in each frame. Incoming images, if not already in the correct resolution, must be scaled (resized) to the resolution of the display device.
Scaling the original image means generating a new image that is larger or smaller than the original. The new image will have a larger or smaller number of pixels in the horizontal and/or vertical directions than the original image. A larger image is scaled up (more new pixels); a smaller image is scaled down (fewer newer pixels). A simple case is a 2:1 increase or decrease in size. A 2:1 decrease could be done by throwing away every other pixel (although this simple method results in poor image quality). For a 2:1 increase, new pixels can be generated in between the old by various interpolation methods.
The scaling process is implemented with various scaling algorithms, also called “filters”, which treat the rows and columns of the image as discrete time-domain signals. When the original image is analog, the image signal is a sampled version of the original analog image and has an inherent sample rate that results in a fixed number of pixels in the image. To rescale the image, it is resampled at some new, predetermined rate to provide the desired number of pixels. This is done by convolving the rows and columns of the original image with various low-pass filter functions to provide sample values at each of the output image pixel locations. Scaling is performed in two passes. Typically, the rows are rescaled first (horizontal scaling), then the new rows are used for resealing the columns (vertical scaling).
A feature of some vertical and horizontal scaling filters is a high frequency emphasis. These scaling filters have stronger gain, greater than unity, in the high-frequency region of their passband than in the low-frequency region of their passband. Although this emphasis can be useful for sharpening the image, it can result in artifacts. High frequency emphasis filtering can also be used for image data that does not require scaling, to achieve image sharpness.